Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique by Lijie Jiang, Qi Li, Huiqing Liao, Hourong Liu, Bowen Tan

“…The novel integration of BWOA for hyper-parameter tuning enhances model precision, advancing prior machine learning efforts in supercritical fluid applications. …”
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A novel simulation and supervised machine learning-based prediction framework to predict the total transportation and energy costs for single-family households by Vinay Gonela, Raghavan Srinivasan, Atif Osmani

“…In the second stage, the simulation model is run several times under varying parameters to develop synthetic data that is used as input for the third stage supervised machine learning (SML) models. …”
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A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing by Peng Zhang, Mengyu Jin, Ming Wang, Jie Zhang, Junjie He, Peng Zheng

“…Batch processing machines are often the bottleneck in semiconductor manufacturing and their scheduling plays a key role in production management. …”
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LiDAR-Based Road Cracking Detection: Machine Learning Comparison, Intensity Normalization, and Open-Source WebGIS for Infrastructure Maintenance by Nicole Pascucci, Donatella Dominici, Ayman Habib

“…This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources.…”
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Developing methodologies to study perceived sound qualities of violins* by Fritz Claudia, Stoppani George, Igartua Unai, Woodhouse Jim

“…Therefore, while we have previously preferred using players in our experiments to maximise ecological validity and account for the complexity of the interaction between the player and the instrument, in this study we test whether other methods that reduce the player's influence, though more artificial, may be useful for exploring the impact of certain construction parameters on the sound. In the context of a set of violins built with controlled thickness variations in their plates, we conducted two listening tests, based on real recordings of a player and a bowing machine, along with synthesised sounds created from an excerpt recorded with piezo sensors by convolution with radiation measurements in an anechoic chamber. …”
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Experimental Investigation and Machine Learning Modeling of Tribological Characteristics of AZ31/B₄C/GNPs Hybrid Composites by Dhanunjay Kumar Ammisetti, Bharat Kumar Chigilipalli, Baburao Gaddala, Ravi Kumar Kottala, Radhamanohar Aepuru, T. Srinivasa Rao, Seepana Praveenkumar, Ravinder Kumar

“…The main aim of the study is to study the effect of various wear parameters (reinforcement percentage (R), applied load (L), sliding distance (D), and velocity (V)) on the wear characteristics (wear rate (WR)) of the AZ91/B₄C/GNP composites. …”
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Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs by Joseph A. Thorsrud, Katy M. Evans, Kyle C. Quigley, Krishnamoorthy Srikanth, Heather J. Huson

“…Given its lack of need for parameter optimization, GBLUP was the most efficient model. …”
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Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning by Keming Mao, Dakang Wang, Shirong Cai, Tao Zhou, Wenxin Zhang, Qianqian Yang, Zikang Li, Xiankun Yang, Lorenzo Picco

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A Novel Approach to Continual Knowledge Transfer in Multilingual Neural Machine Translation Using Autoregressive and Non-Autoregressive Models for Indic Languages by Shailashree K. Sheshadri, Deepa Gupta, Biswajit Paul, J. Siva Bhavani

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Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks by Yangchen Li, Lingzhi Zhao, Tianle Wang, Lianghui Ding, Feng Yang

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Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study by Youngjun Lee, Wikum R. Bandara, Sangjun Park, Miran Lee, Choongboem Seo, Sunwoo Yang, Kenneth J. Lim, Sharon M. Moe, Stuart J. Warden, Rachel K. Surowiec

“…Model 4 developed from the diaphyseal tibia region excelled in classifying test and independent validation datasets, achieving F1 scores of 0.99 and 0.96, AUCs of 0.99 and 0.94, sensitivities of 0.99, and specificities of 0.99 and 0.92. No single parameter, including BMD and cortical porosity, among conventional CT outcomes consistently differentiated CKD from non-CKD across all anatomical sites.Integrating HRpQCT with deep and machine learning, this innovative approach enables precise automatic segmentation of severely deteriorated endocortical surfaces and enhances sensitivity to CKD-related cortical bone changes compared to standard DXA and HRpQCT outcomes.…”
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In Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compounds by P. M. Vassiliev, A. V. Golubeva, A. R. Koroleva, M. A. Perfilev, A. N. Kochetkov

“…The literature review showed that the most widely used methods for in silico assessment of the ADMET parameters of pharmacologically active compounds included the random forest method and the support vector machines method. …”
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Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retro... by Li Zhang, Ling He, Guangli Zhang, Xiaoyin Tian, Haoru Wang, Fang Wang, Xin Chen, Yinglan Zheng, Man Li, Yang Li, Zhengxiu Luo

“…Objectives To develop a model incorporating computed tomography (CT) radiomic features and clinical parameters for predicting bronchiolitis obliterans (BO) with adenovirus pneumonia in children. …”
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RESEARCH ON MULTI-BODY DYNAMIC SIMULATION OF HUMANOID FOOT TYPE STAIR CLIMBING WHEELCHAIR BASED ON ADAMS by ZHU Hua, YANG Hui, HUANG ZhenLi, GUO ChangJian, YU YiPeng

“…In order to apply the rotation pairs of the front and back wheels of the wheelchair of the ADAMS and IF function and AZ function,many simulation experiments are carried out to simulate the parameters of the driving motor and the mechanical parameters of the mechanical legs. …”
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Topology for gaze analyses - Raw data segmentation by Oliver Hein, Wolfgang H. Zangemeister

“…The method, namely identification by topological characteristics (ITop), is parameter-free and needs no pre-processing and post-processing of the raw data. …”
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Airflow Cooling Mechanism for High Power-Density Permanent Magnet Motor by Awungabeh Flavis Akawung, Besong John Ebot, Yasutaka Fujimoto

“…High power-density electric machines present the benefits of high torque and speed. …”
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Optimization research on laminated cooling structure for gas turbines: A review by Xiaojing Tian, Weiqi Ye, Liang Xu, Anjian Yang, Langming Huang, Shenglong Jin

“…The experimental and simulation studies therein were reviewed, and the major influencing parameters in the structure were analyzed in detail. …”
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A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition by Qian Qiao, Quan Liu, Jiong Pu, Haixia Shi, Wenxiao Li, Zhixiong Zhu, Dawei Guo, Hongchang Qian, Dawei Zhang, Xiaogang Li, C. T. Kwok, L. M. Tam

“…The recent exploration of machine learning (ML) exhibits great potential to obtain a suitable balance between productivity and set parameters. …”
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Multi-Objective Decision Making for Z Coordinator and Overcut in µ - EDM process using Tungsten Carbide Electrode for machining of Titanium Alloy by Phan Huu Nguyen

“…It led to improve machining performance like machining accuracy, reduced electrode wear and improved surface quality. …”
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Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment by A. Koraniany, G.R. Ansarifar

“…The obtained results were employed to develop a machine learning-based artificial neural network in MATLAB. …”
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